International Journal of Computer Science & Network Security

국제컴퓨터통신보호논문지학회 (International Journal of Computer Science & Network Security)
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An Intelligent Gold Price Prediction Based on Automated Machine and k-fold Cross Validation Learning

  • Baguda, Yakubu S. (Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University) ;
  • Al-Jahdali, Hani Meateg (Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University)
  • 투고 : 2021.04.05
  • 발행 : 2021.04.30
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초록

The rapid change in gold price is an issue of concern in the global economy and financial markets. Gold has been used as a means for trading and transaction around the world for long period of time and it plays an integral role in monetary, business, commercial and financial activities. More importantly, it is used as economic measure for the global economy and will continue to play an important economic vital role - both locally and globally. There has been an explosive growth in demand for efficient and effective scheme to predict gold price due its volatility and fluctuation. Hence, there is need for the development of gold price prediction scheme to assist and support investors, marketers, and financial institutions in making effective economic and monetary decisions. This paper primarily proposed an intelligent based system for predicting and characterizing the gold market trend. The simulation result shows that the proposed intelligent gold price scheme has been able to predict the gold price with high accuracy and precision, and ultimately it has significantly reduced the prediction error when compared to baseline neural network (NN).

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과제정보

The authors would like to thank all those who contributed toward making this research successful. Also, we would like to thanks the reviewers for their insightful and valuable comment. This work is supported by the Deanship of Scientific Research (DSR), King Abdulaziz University, Saudi Arabia, under grant D-64-830-1437. The authors are very grateful to the DSR for their technical and financial support throughout the period of the project.

참고문헌

  1. Y. Long, "Visibility graph network analysis of gold price time series. Physica A. 2013;392(19):3374-3384. https://doi.org/10.1016/j.physa.2013.03.063
  2. A. Parisi, F. Parisi, and D. Diaz, "forecasting gold price changes: rolling and recursive neural network models. Journal of Multinational Financial Management. 2008;18(5):477-487. https://doi.org/10.1016/j.mulfin.2007.12.002
  3. S. Shafiee, and E. Topal, "An overview of global gold market and gold price forecasting. Resources Policy. 2010;35(3):178-189. https://doi.org/10.1016/j.resourpol.2010.05.004
  4. S. M. Idrees, M. A. Alam, P. Agarwal, "A prediction approach for stock market volatility based on time series data", IEEE Access, vol. 7, pp. 17287-17298, 2019. https://doi.org/10.1109/ACCESS.2019.2895252
  5. M. Khashei, M. Bijari, "An artificial neural network (p d q) model for timeseries forecasting", Expert Syst. Appl., vol. 37, no. 1, pp. 479-489, Jan. 2010. https://doi.org/10.1016/j.eswa.2009.05.044
  6. Y. Chen, W. Lin, and J. Z. Wang "A Dual-Attention-Based Stock Price Trend Prediction Model With Dual Features," IEEE Access, Vol. 7, 2019
  7. I. Sutskever, O. Vinyals, Q. V. Le, "Sequence to sequence learning with neural networks", Proc. Adv. Neural Inf. Process. Syst., pp. 3104-3112, 2014.
  8. J. Ngiam, A. Khosla, M. Kim, J. Nam, H. Lee, and A. Y. Ng, 'Multimodal deep learning," in Proc. 28th Int. Conf. Mach. Learn., 2011, pp. 689-696.
  9. R. C. Cavalcante, R. C. Brasileiro, V. L. F. Souza, J. P. Nobrega, A. L. I. Oliveira, "Computational intelligence and financial markets: A survey and future directions", Expert. Syst. Appl., vol. 55, pp. 194-211, Aug. 2016. https://doi.org/10.1016/j.eswa.2016.02.006
  10. P. Moradi and M. Gholampour, "A hybrid particle swarm optimization for feature subset selection by integrating a novel local search strategy," Appl.Soft. Comput., vol. 43, pp. 117_130, Jun. 2016. https://doi.org/10.1016/j.asoc.2016.01.044
  11. H. Dong, T. Li, R. Ding, and J. Sun, "A novel hybrid genetic algorithm with granular information for feature selection and optimization," Appl.Soft. Comput., vol. 65, pp. 33_46, Apr. 2018. https://doi.org/10.1016/j.asoc.2017.12.048
  12. P. C. Chang, C. Y. Fan, and C. H. Liu, "Integrating a piecewise linear representation method and a neural network model for stock trading points prediction," IEEE Trans. Syst., Man, C (Appl. Rev.), vol. 39, no. 1, pp. 80_92, Jan. 2009. https://doi.org/10.1109/TSMCC.2008.2007255
  13. L. Zhao and L.Wang, "Price trend prediction of stock market using outlier data mining algorithm," in Proc. IEEE 5th Int. Conf. Big Data Cloud Comput., Aug. 2015, pp. 93_98.
  14. S. K. Chandar, "Fusion model of wavelet transform and adaptive neuro fuzzy inference system for stock market prediction," in Journal of Ambient Intelligence and Humanized Computing. Berlin, Germany: Springer, 2019.
  15. J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, "BERT: Pretraining of deep bidirectional transformers for language understanding," 2018, arXiv:1810.04805, [Online]. Available: https://arxiv.org/abs/1810.04805
  16. X. Ding, Y. Zhang, T. Liu, and J. Duan, "Deep learning for eventdriven stock prediction," in Proc. 24th Int. Joint Conf. Artif. Intell., Jun. 2015, pp. 2327_2333.
  17. Y. Qin, D. Song, H. Chen,W. Cheng, G. Jiang, and G.W. Cottrell, "A dualstage attention-based recurrent neural network for time series prediction," in Proc. 26th Int. Joint Conf. Artif. Intell., Aug. 2017, pp. 2627_2633.
  18. D. M. Nelson, A. C. Pereira, and R. A. de Oliveira, "Stock market's price movement prediction with LSTM neural networks," in Proc. Int. Joint Conf. Neural Netw. (IJCNN), May 2017, pp. 1419_1426.
  19. X. Pang, Y. Zhou, P. Wang, W. Lin, and V. Chang, "An innovative neural network approach for stock market prediction," in The Journal of Super-computing. Springer, 2018. doi: 10.1007/s1122.
  20. F. Black, M. C. Jensen, M. Scholes, "The capital asset pricing model: Some empirical tests", Stud. Theory Capital Markets, vol. 81, no. 3, pp. 79-121, 1972.
  21. E. F. Fama, K. R. French, "Common risk factors in the returns on stocks and bonds", J. Financial Econ., vol. 33, no. 1, pp. 3-56, 1993, https://doi.org/10.1016/0304-405X(93)90023-5
  22. A. Krizhevsky, I. Sutskever, G. E. Hinton, "Imagenet classification with deep convolutional neural networks", Commun. ACM, vol. 60, no. 2, pp. 2012, 2012.
  23. R. C. Chung, W. Ip, S. Chan, "An ARIMA-intervention analysis model for the financial crisis in China's manufacturing industry", Int. J. Eng. Bus. Manage., vol. 1, pp. 5, Mar. 2009.
  24. B. Guha, G. Bandyopadhyay, "Gold price forecasting using ARIMA model", J. Adv. Manage. Sci., vol. 4, no. 2, pp. 117-121, Mar. 2016.
  25. A. Mohamed, T. N. Sainath, G. Dahl, B. Ramabhadran, G. E. Hinton, M. A. Picheny, "Deep belief networks using discriminative features for phone recognition", Proc. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP), pp. 5060-5063, May 2011.
  26. J. Zhou, Y. Lu, H.-N. Dai, H. Wang, H. Xiao, "Sentiment analysis of chinese microblog based on stacked bidirectional LSTM", IEEE Access, vol. 7, pp. 38856-38866, 2019. https://doi.org/10.1109/ACCESS.2019.2905048
  27. Z. Zheng, Y. Yang, X. Niu, H.-N. Dai, Y. Zhou, "Wide and deep convolutional neural networks for electricity-theft detection to secure smart grids", IEEE Trans. Ind. Informat., vol. 14, no. 4, pp. 1606-1615, Apr. 2018. https://doi.org/10.1109/tii.2017.2785963
  28. T. Kim, H. Y. Kim, "Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data", PLoS ONE, vol. 14, no. 2, pp. 1-23, Feb. 2019.
  29. V. Mnih, N. Heess, A. Graves, K. Kavukcuoglu, "Recurrent models of visual attention", Proc. 27th Int. Conf. Neural Inf. Process. Syst. (NIPS), vol. 2, pp. 2204-2212, 2014
  30. R. He, W. S. Lee, H. T. Ng, D. Dahlmeier, "An unsupervised neural attention model for aspect extraction", Proc. 55th Annu. Meeting Assoc. Comput. Linguistics Long Papers, vol. 1, pp. 388-397, Jul. 2017
  31. N. Si, H. Wang, W. Li, Y. Shan, "Chinese POS tagging with attentionbased long short-term memory network" in Advances in Intelligent Systems and Interactive Applications, Cham, Switzerland:Springer, pp. 586-592, Jun. 2018.
  32. G. E. P. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung, Time Series Analysis: Forecasting and Control. Hoboken, NJ, USA: Wiley, 2015.
  33. T. Fischer and C. Krauss, "Deep learning with long short-term memory networks for _nancial market predictions," Eur. J. Oper. Res., vol. 120, no. 2, 2017.
  34. G. Lai, W.-C. Chang, Y. Yang, and H. Liu, "Modeling long-and short-term temporal patterns with deep neural networks," in Proc. 41st Int. ACM SIGIR Conf. Res. Develop. Inf. Retr., 2018.
  35. S. Jeon, B. Hong, and V. Chang, "Pattern graph tracking-based stock price prediction using big data," Future Gener. Comput. Syst., vol. 80, 2018.
  36. X. Xu, J. Zhang, and M. Small, "Superfamily phenomena and motifs of networks induced from time series," Proc. Nat. Acad. Sci., vol. 105, no. 50, 2008. https://doi.org/10.1073/pnas.0810765106
  37. L. Zhang, C. Aggarwal, and G.-J. Qi, "Stock price prediction via discov-ering multi-frequency trading patterns," in Proc. 23rd ACM SIGKDD Int.Conf. Knowl. Discovery Data Mining, 2017.
  38. W. Huang, Y. Nakamori, and S.-Y. Wang, "Forecasting stock market movement direction with support vector machine," Comput. Oper. Res.,vol. 32, no. 10, 2005.
  39. K. J. Kim, "Financial time series forecasting using support vector machines," Neurocomputing, vol. 55, nos. 1_2, 2003.
  40. D. T. Tran, J. Kanniainen, M. Gabbouj, and A. Iosi_dis. (2019). Datadriven neural architecture learning for _nancial time-series forecasting." [Online]. Available: https://arxiv.org/abs/1903.06751
  41. S. Walczak, "An empirical analysis of data requirements for financial forecasting with neural networks," J. Manage. Inf. Syst., vol. 17, no. 4, 2001.
  42. 26. Gold price dataset (1968-2017). [online] Available: https://www.kaggle.com/gwhittington/lbma-gold-price-19682017 (Accessed December 2018)
  43. M. Almasarweh, S. Alwadi, "ARIMA model in predicting banking stock market data", Modern Appl. Sci., vol. 12, no. 11, pp. 309, 2018. https://doi.org/10.5539/mas.v12n11p309